Learning of motor maps from perception: a dimensionality reduction approach

Ankit Awasthi, IIT Kanpur

Sadbodh Sharma, IIT Kanpur

Amitabha Mukerjee, IIT Kanpur

Abstract

The role of perception in sighted infant motor development is
well-established, but what are the processes by which an infant
manages to handle the complex high-dimensional visual
input? Clearly, the input has to be modeled in terms of lowdimensional
codes so that plans may be made in a more abstract
space. While a number of computational studies have
investigated the question of motor control, the question of how
the input dimensionality is reduced for motor control purposes
remains unexplored. In this work we propose a mapping where
starting from eye-centered input, we organize the perceptual
images in a lower-dimensional space so that perceptually similar
arm poses remain closer. In low-noise situations, we find
that the dimensionality of this discovered lower-dimensional
embedding matches the degrees-of-freedom of the motion.
We further show how complex reaching and obstacle avoidance
motions may be learned on this lower-dimensional motor
space. The computational study suggests a possible mechanism
for models in psychology that argue for high orders of
dimensionality reduction in moving from task space to specific
action